Increasing demand for data transmissions in wireless networks has increased the need for higher throughput systems. High order modulation and/or MIMO set-up can address the demand for high throughput. For example, 256-QAM signaling has been adopted by the 3Rd Generation Partnership Project (3GPP) group in Long Term Evolution (LTE)-Release 12 to increase LTE system throughput. Further, the currently developing Institute of Electrical and Electronics Engineers (IEEE) 802.11ax standard is considering 1024-QAM to further increase Wi-Fi throughput.
However, hardware implementation complexity of maximum likelihood (ML) detection of MIMO channels increases exponentially with the number of transmitted layers and modulation order, which makes real-world hardware implementation infeasible. For example, the hardware complexity for soft detection of 256-QAM MIMO with two transmitted layers is roughly 16 times the hardware complexity of 64-QAM MIMO with two transmitted layers. Therefore, the use of sub-optimal schemes is inevitable in practical hardware implementation.
Some sub-optimal detection schemes have already been introduced. The most common scheme which reduces optimal ML complexity is obtained with max-log-MAP (MLM) approximation. However, high order modulation signaling such as 256-QAM still makes hardware implementation of MLM scheme infeasible.
Soft list sphere decoding (LSD) has also been introduced as an alternative to further reduce complexity in soft detection of coded MIMO channels. However, the variable complexity of LSD, as well as the complexity of search space selection of LSD, introduce new challenges in hardware implementation.